How to build a DQN agent which can be trained through interactive learning?
I suggest you to start reading this one on BERT and this one on GPT-2.


  I am aware this question seems too broad, but some hints will be very helpful for a newbie like me.


I'm not sure you want to create your chatbot using RL architecture at all. But if you want to implement such an idea the right way to go with that is by using iterative approach and starting from a really simple base:


Define a list of actions for your agent. Might be 10-words
vocabulary with simple one-word answers. 
Define you simple environment. It might have 20 different states (e.g. greetings/conversation starters hey, hello)
Map good responses (hello -> hi) to positive rewards and -1 for the rest of them.
If you want to move forward with DQN try a simple Q-learning algorithm on your simple environment and train agent to properly response to those.
Once you have done the above you can make your vocabulary bigger
Now replace your table of input words which gives you a state for your network with NN/word2vec/any other NLP approach which will convert your input sentence to a state.
Manually provide a reward as a response for what you agent did according to learned policy.
Do that infinite amount of time and your agent probably will figure out how to respond properly.


NOTE: DQN uses discrete action space. So in such a case you will have only limited or REALLY HUGE actions space. It's all possible combinations between all possible words in you vocabulary (assuming action maps to some sentence)